Fraud_detection | Kaggle

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Fraud_detection_

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Notebook

Input

DATASETS

credit-card-fraud-detection-dataset-2023
- creditcard_2023.csv

Output (72KiB / 19.5GiB)

/kaggle/working

Table of contents

Step 1: Load the Data and Learn About It Step 2: Exploratory Data Analysis (EDA)Step 3: Define and Train Models 3.3 Performance Comparison of Classification Models

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Connect to BigQuery
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Connect to Google Cloud Storage
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Connect to AutoML
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Define a generic user secret
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Display an image
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Read a JSON file
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Automatic EDA with pandas-profiling
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Save a file to your working directory
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Use legacy SQL syntax with BigQuery
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Train a decision tree
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Plot null values
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See source code for a function
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Visualize a decision tree
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Scatter plots with plotly express
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Bubble plots with plotly express
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Replace DataFrame null values with mean values
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Read a CSV file
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List the contents of your input directory
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List installed packages
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Write to CSV
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Create and compile a TensorFlow Sequential model
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Create and compile a TensorFlow Functional model
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Run a TensorFlow model on a TPU
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Set up keras-tuner RandomSearch
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Set up keras-tuner Hyperband
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get CSV data from a URL with TensorFlow/Keras
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Build a TF Dataset from Numpy arrays
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Build a TF Dataset from a dictionary
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Tokenize text with Tensorflow/Keras
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Load a Tensorflow Dataset
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Load a pretrained ImageNET Classifier
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Binary classification in TensorFlow
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Build a regression model in TensorFlow
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Apply a transformation to a tensor
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Iterate over a tensor
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Batch a dataset
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Get GCS path for Kaggle datasets
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Import a pretrained model
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Classification with imbalanced data
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Load images from a file directory
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Exponential decay learning rate scheduler callback
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Model checkpoint callback
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Visualize model performance
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Trim a trailing newline

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